Variation in breeding practices and geographic isolation drive subpopulation differentiation, contributing to the loss of genetic diversity within dog breed lineages
Bibliographic record
Abstract
BACKGROUND: Discrete breed ideals are not restricted to delimiting dog breeds from another, but also are key drivers of subpopulation differentiation. As genetic differentiation due to population fragmentation results in increased rates of inbreeding and loss of genetic diversity, detecting and alleviating the reasons of population fragmentation can provide effective tools for the maintenance of healthy dog breeds. RESULTS: Using a genome-wide SNP array, we detected genetic differentiation to subpopulations in six breeds, Belgian Shepherd, English Greyhound, Finnish Lapphund, Italian Greyhound, Labrador Retriever and Shetland Sheepdog, either due to geographical isolation or as a result of differential breeding strategies. The subpopulation differentiation was strongest in show dog lineages. CONCLUSIONS: Besides geographical differentiation caused by founder effect and lack of gene flow, selection on champion looks or restricted pedigrees is a strong driver of population fragmentation. Artificial barriers for gene flow between the different subpopulations should be recognized, their necessity evaluated critically and perhaps abolished in order to maintain genetic diversity within a breed. Subpopulation differentiation might also result in false positive signals in genome-wide association studies of different traits. LAY SUMMARY: Purebred dogs are, by definition, reproductively isolated from other breeds. However, similar isolation can also occur within a breed due to conflicting breeder ideals and geographic distances between the dog populations. We show here that both of these examples can contribute to breed division, with subsequent loss of genetic variation in the resulting breed lineages. Breeders should avoid creating unnecessary boundaries between breed lineages and facilitate the exchange of dogs between countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".